Occupancy estimation within a particular region is useful in a variety of applications. For example, information regarding occupancy of a building or region within a building can be used to improve the efficiency, comfort, and convenience of the building for occupants, for example by turning on lights or activating heating/cooling systems. Occupancy estimation within a building may also be utilized to direct first responders in the event of an emergency such as a fire. In other applications, occupancy information can be utilized by Wi-Fi service providers to optimize performance in a given region (indoor or outdoor). More generically, occupancy estimation can be utilized to estimate the number of customers in a store or visitors to attraction, plan resources in a smart city.
A variety of occupancy estimation systems have been developed over the years. Examples of such systems include video monitoring/analytics systems, radio-frequency identification (RFID) systems, as well as others. However, each of these systems suffers from one or more drawbacks. For example, video monitoring/analytics systems require a considerable amount of additional equipment, including video camera and large amounts of memory for storing the video data to be analyzed. In addition, video analytic systems suffer from notoriously poor performance in low-light conditions. Most importantly, they do not preserve privacy and do not have the potential of counting behind walls. Other systems, such as RFID systems rely on occupants carrying an RFID tag/card that can be sensed by the RFID system to monitor occupants. However, this system requires distribution of RFID card to occupants, which is not feasible in many applications.
It would therefore be desirable to develop a system and method that is able to monitor occupancy in a variety of different applications, without relying on people to carry any device, while preserving privacy, and with see-through capability.